Dynamic Network Visualization in 1.5D Lei Shi *, Chen Wang *, Zhen Wen † * IBM Research – China † IBM T.J. Watson Research Center

Mobile SMS Network – Spammer

Mobile SMS Network – Non-Spammer

Mobile SMS Network – Spammer/Non-Spammer

Outline Problem Related Works & Previous Solutions Data Processing Dynamic Ego Network Event-based Dynamic Networks Visualization Metaphor Graph layouts Interactions Case Study Mobile SMS Networks Infovis/VAST Conferences

Background & Research Problem Dynamic networks are overwhelming in the reality, big value add-on with visualization Demonstrate huge evolving social network over SNS/Twitter for community detection Show the dynamically changing ad-hoc-routing sensor networks for diagnosis purpose Visual evidence of growing telecom networks for role identification: employee retention Problem with dynamic network visualization How to encode the time dimension 3D? Video? Summarization? How to deal with scalability Finer time granularity => Larger network complexity => (visual clutter, bigger computation cost) Usability for interactive analytics Help automate pattern discovery Advanced text trend visualization Generic theme definitions: topics, words, categories, classifications Content representations: single word/phrase/sentence -> multiple list of keywords summarizing content of different time segments Comprehensive text data model and vis-data mapping Cover most text data corpus compared to related works Flexible representation of text data facets Effective applications to multiple domains with the unified approach Leverage interactions for insight finding Integrated visual text analytics system with online data processing, retrieval and visualization Scalable to large size and incremental scenario Easy support of different applications only by replacing the data index Generic visualization appliance for dynamic textual data Support textual data with only time facet up to many facets

Related Works: Dynamic Movie Approach

Related Works: Small Multiple Display

Related Works: Dynamic Graph Drawing Objective: preserve the user’s mental map [ELM91][MEL95] Orthogonal ordering Proximity relationships Topology Mental-map preserving dynamic graph drawing algorithms Online dynamic graph drawing algorithms: compute the layout of one time frame only from its previous time frame and the graph change Graph adjustment, e.g. force-scan algorithm [MEL95] Extension from KK model [BBP07] Incremental graph layout [North95][DKM06] Offline dynamic graph drawing algorithms: take all the graphs in previous time frame into consideration Optimize global stability [DGK01][CKN03] Encode the graph change in multi-layer representation [BC02] Special graph/drawing types Hierarchical graph [North95][NW02], clustered graph [HEW98][FT04] Orthogonal graph [PT98][GBP04], radial graph [YFD01]

1.5D Dynamic Network Visualization Basic idea: only consider the dynamic ego network central to one node Many network analytics applications are egocentric: person role analysis, company collaborations analysis Rationality: demultiplex the data in network domain (1.5D Vis) v.s. time domain (movie approach) v.s. space domain (small multiple displays) Benefits: Fit both time and network info into a single static 2D visualization (0.5D time, 1.5D network) Reduced network size and layout computation complexity, less visual clutter Better support dynamic network analytics, e.g. temporal network pattern discovery Trade-offs: Will lose the overall graph topology semantics and the topology evolving patterns Compensate a little with interactions

Visual Metaphor Horizontal Glyph 2-hop node central node sending/receiving trend Radial Glyph 1-hop node time-dependent edge time-independent edge

Data Processing for 1.5D Visualization 3 steps to generate the dynamic ego network data for 1.5D visualization Slotting: Extraction: reduce each slotted graph into the ego graph central to the selected node Compression: aggregate the ego graphs into a single graph with time- dependent and time-independent edges Event-based dynamic networks Insertion: the new event node is added to the graph, an edge is added between the event node and existing nodes if this event ever happens to it at a specific time

Graph Layout Customized force-directed layout model for small/medium-sized networks: Split the central trend node into several sub- nodes Fix the sub-node locations at Y axis Add stability constraints to non-central nodes to place them near their average time to the center A balance of time-dependent and time- independent edge forces Circular graph layout for large networks Partition Sort Assign

Graph Interactions Timeline navigation Egocentric graph navigation zoom & pan zoom drill-in to new central node view

Case Study — Mobile SMS Network For each people, send only one message in one time For some people, send multiple messages in multiple times

Case Study — Mobile SMS Network Unidirectional communication (no reply) Bidirectional communication (send & reply)

Case Study — Mobile SMS Network No communications between receivers (friends) Connections between receivers (friends)

Case Study — Mobile SMS Network Smooth transmissions (the automatic scanning with powerful machine) Irregular transmission pattern

Case Study — Conference Author Networks Infovis author network: ego-edge mode, Prof. Stasko’s network

Case Study — Conference Author Networks Infovis author network: network-edge mode Dr. Wong’s network Prof. Munzner’s network

Case Study — Conference Author Networks VAST author network Overview Prof. Ribarsky’s network

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